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              <p><strong>UNIVERSITY OF ENGINEERING &amp; TECHNOLOGY </strong></p>
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              <p><strong>UNIVERSITY OF SCIENCE</strong></p>
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              <p><strong>HANOI NATIONAL UNIVERSITY OF EDUCATION</strong></p>
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          <div class="mc01t2"> CONFERENCE COMMITTEE </div>
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            <div class="mcl02 style2">Honorary Chairs</div>
            Dinh-Tri Nguyen, VNU-IFI, Vietnam<br />
            Roberto deMarca, IEEE President<br />

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            Tu-Bao Ho, JAIST, Japan <br />
            Piuri Vincenzo, Milan University, Italy<br />
            
			<div class="mcl02 style2">Programme Chairs</div>
            Thanh-Thuy Nguyen, VNU-UET, Vietnam<br />
            Mizuhito Ogawa, JAIST, Japan<br />
            
			<div class="mcl02 style2">Organizing Chairs</div>
            Bao-Son Pham, VNU-UET, Vietnam<br />Anh-Cuong Le, VNU-UET, Vietnam<br/>
	    Cam-Ha Ho, HNUE, Vietnam<br />
            Thi-Minh-Huyen Nguyen, VNU-HUS, Vietnam <br />
            Xuan-Tu Tran, VNU-UET, Vietnam <br />
            Tuong-Vinh Ho, VNU-IFI, Vietnam <br />

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            Anh-Cuong Le, VNU-UET, Vietnam<br />
            Marc Bui, University Paris 8, France<br />

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            Hi-Duc Pham, ECE, France<br />
            Thi-Ha-Duong Phan, VAST, Vietnam<br />

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            Xuan-Tu Tran, VNU-UET, Vietnam<br />
	    Xuan-Hieu Phan, VNU-UET, Vietnam<br />
            Bao-Quoc Ho, VNU-HCMUS, Vietnam<br />

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            Nim Cheung, Hong Kong, China<br />
            John Vig, IEEE, USA<br />
            Janina Mazierska, Massey University, New Zealand<br />
            Dinh-Tri Nguyen, VNU-IFI, Vietnam<br />
            Byeong Ji Lee, Seoul National University, Korea<br />
            Jean-Marc Steyeart, Ecole Polytechnique, France<br />
			Takuya Katayama, JAIST, Japan<br />
			
			<div class="mcl02 style2">Conference Organizers</div>
            IEEE Vietnam Section<br />
            VNU University of Engineering and Technology (VNU-UET)<br />
            VNU Institut Francophonie de l'Informatique (VNU-IFI)<br />
            Hanoi University of Education<br />

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            IEEE Communications Society<br />
            IEEE Computational Intelligence Society<br />
            
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        <center><p style='font-size: 20px; color: #F00; font-weight: bold; font-family: "Palatino Linotype", "Book Antiqua", Palatino, serif'>KEYNOTE TALKS</p></center>
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            <td><img src="image/QiangYang.jpg"></td>
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              <h2 style="padding: 0; margin: 0">Prof. Qiang Yang</h2>
              (Hong Kong University of Science and Technology)
              <br>Big Data, Lifelong Machine Learning and Transfer Learning
              <br><a target="_blank" href="http://pakdd2013.pakdd.org">http://pakdd2013.pakdd.org</a>
              <p><a href="slides/RIVF2013-Keynote-Qiang Yang.pdf" target="_blank"><img src="image/pdf.jpg" height="20px"> Download slides</a></p>
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            <td colspan="2">
              <h3>Huawei and HKUST</h3>
              <small>Prof. Qiang Yang is the head of Huawei Noah’s Ark Lab in Hong Kong. He has been a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) since 2007. Prior to joining HKUST, he had been a faculty member at the University of Waterloo and Simon Fraser University in Canada. He is an IEEE Fellow, IAPR Fellow and ACM Distinguished Scientist. His research interests are data mining and artificial intelligence. Qiang received his PhD from the University of Maryland, College Park in 1989. His research teams won the 2004 and 2005 ACM KDDCUP competitions on data mining. He is the vice chair of ACM SIGART, the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST), and organizer for many international conferences and workshops, including the PC Co-chair for ACM KDD 2010, the General Chair for ACM KDD 2012 in Beijing and PC Chair for IJCAI 2015 Conference in Argentina.</small>
              <h3>Big Data, Lifelong Machine Learning and Transfer Learning</h3>
              <small>A major challenge in today's world is the Big Data problem, which manifests itself in Web and Mobile domains as rapidly changing and heterogeneous data streams.  A data-mining system must be able to cope with the influx of changing data in a continual manner.  This calls for Lifelong Machine Learning, which in contrast to the traditional one-shot learning, should be able to identify the learning tasks at hand and adapt to the learning problems in a sustainable manner.  A foundation for lifelong machine learning is transfer learning, whereby knowledge gained in a related but different domain may be transferred to benefit learning for a current task.  To make effective transfer learning, it is important to maintain a continual and sustainable channel in the life time of a user in which the data are annotated. In this talk, I outline the lifelong machine learning situations, give several examples of transfer learning and applications for lifelong machine learning, and discuss cases of successful extraction of data annotations to meet the Big Data challenge.</small>
              <br><hr><br>
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              <h2 style="padding: 0; margin: 0">Prof. Douglas N. Zuckerman</h2>
              (Applied Communication Sciences, USA)
              <br>Cloud Computing Under the IEEE Umbrella
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              <h3>Biography</h3>
              <small>Douglas N. Zuckerman received his B.S., M.S. and Eng.Sc.D degrees in Electrical Engineering from Columbia University (USA) in 1969, 1971 and 1976, and is an IEEE Life Fellow. His over 30 years of experience, mainly at Bell Labs and Telcordia Technologies, span the operations, management and engineering of emerging network technologies and services. He is currently on the IEEE Board of Directors as the Division III (Communications Technology) Director. He was the IEEE Communications Society President in 2008-9.</small>
              <br><br><small>His technical career included long-haul millimeter waveguide studies (before fiber), satellite systems engineering, maintenance engineering for the world’s first digital transmission networks, business services operations planning, and most recently IP-centric optical network interoperability. He was an early contributor to TMN standards and had chaired the Optical Internetworking Forum’s OAM&P Working Group. He currently chairs the IEEE Cloud Computing Initiative’s conferences track.</small>
              <br><br><small>For over 25 years, Doug's leadership across ComSoc's technical committees, conferences, publications, chapters and Society governance has maintained focus on member interests worldwide, especially making relevant technical information widely and quickly available on line and in conferences, and encouraging more member interaction in the technical committees. He co-founded technical committees on Network Operations & Management and Enterprise Networking, as well as the IEEE Network Operations & Management Symposium (NOMS).</small>
              <br><br><small>His sustained contributions were recognized through the Salah Aidarous Memorial Award, the Society's Donald McLellan Meritorious Service Award, its Conference Achievement Award and the IEEE Third Millennium Medal.</small>
              <h3>Cloud Computing Under the IEEE Umbrella</h3>
              <small>Cloud computing already has widespread impact across how we access today’s applications, resources, and data. Public, private and hybrid clouds offer many services, generally referred to as XaaS (e.g., X = S for “Software as a Service”). Yet, many issues around cloud computing need to be addressed, including, but not limited to, security, reliability, architecture, and economics. The IEEE has established a Cloud Computing Initiative (CCI) to serve as an umbrella for the wide range of global activities aimed at addressing these issues. Under its umbrella are standards (e.g., IEEE P2301 and P2302); conferences (e.g., RIVF 2013); publications (e.g., IEEE Trans. on Cloud Computing); education and careers (e.g., Webinars); Intercloud test beds (e.g., for P2302); and the relationship with big data, Internet of Things (IoT) and IPv6. Based on progress enabled by this Initiative, this presentation will give an overview of “the Cloud,” discuss open issues and solutions for cloud computing, communications and networking, and highlight the role of software-defined networking (SDN) as part of cloud architectures.</small>
              <br><hr><br>
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              <h2 style="padding: 0; margin: 0">Prof. Akihiko Takano</h2>
              (National Institute of Informatics, Japan)<br>
              From Search to Association, bridging the isolated silos of knowledge
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              <h3>Biography</h3>
              <small>Prof. Akihiko Takano is the Director of Research Center for Informatics of Association, National Institute of Informatics, Japan. He received Ph.D. in Computer Science from University of Tokyo in 2000, after working at research laboratories in Hitachi, Ltd. for more than twenty years. His research interests are functional programming, program transformation, partial evaluation and informatics of association. He joined National Institute of Informatics in 2001 and has served as a professor also at the University of Tokyo since 2002, Ritsumeikan University since 2012. He is enthusiastic about building the associative information services based on Generic Engine for Transposable Association.</small>
              <h3>From Search to Association, bridging the isolated silos of knowledge</h3>
              <small>Memory institutes such as Libraries, Archives, and Museums have been responsible for preserving the records and the memory of our cultures in physical form. They try continue to be the centers for digital form of records and create various form of digital archives. But most of their information are organized separately and maintained in the isolated silos of knowledge. The primary goal of our research, "Informatics of Association", is to bridge these isolated silos in meaningful and  human friendly ways.</small>
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              <h2 style="padding: 0; margin: 0">Prof. Jean-Daniel Zucker</h2>
              <br>Machine Learning Challenges in Metagenomics
              <p><a href="slides/RIVF2013-Keynote-Zucker.pdf" target="_blank"><img src="image/pdf.jpg" height="20px"> Download slides</a></p>
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              <h3>Biography</h3>
              <small>Jean-Daniel Zucker is a former Engineer (ESIAE, 1985) in Computer Science and Aeronautical Engineering. He then received in 1986 a Master in Artificial Intelligence applied to Life Science. He worked for the New England Medical Center (Boston, USA), IBM and Thomson in R&D for six years. After a Master in Artificial Intelligence (Major in Machine Learning) in 1992 he got is PhD. in 1996 in Machine Learning from Paris 6 University where he became an assistant and then associate professor focusing on representation changes and abstraction in machine learning. In 2002 he became Full Professor of Computer Science at Paris 13 University where he led a CNRS  (the French NSCF) team on Machine Learning and Transcriptomics. In 2008 he became a Senior Researcher at the National Institute of Research for Development (IRD) working on Data Mining and Complex Systems. He is deputy director of the Complex Systems Laboratory UMMISCO UMI 209 (IRD and University Pierre and Marie Curie in Paris, France). At present he is posted in Hanoi Vietnam and participates to several projects in South-East Asia and Europe. In the past twenty years his main research interest has been Machine Learning, Data Mining and Multi-agent Modeling with applications ranging from Post-genomics to Environmental Decision Support systems. His Google Scholar h-index is 26, he has written more than 200 publications including 46 in peer reviewed journals. He co-authored very recently a book published by Springer “<b>Abstraction in Artificial Intelligence and Complex Systems</b>”.</small>
              <h3>Machine Learning Challenges in Metagenomics</h3>
              <small>High-throughput technologies and today Next Generation Sequencing (NGS) have allowed the production of large genomic datasets: for instance, microarray data contain the simultaneous expression of tens of thousands of genes or NGS data may reach several millions of genes counts. In this talk we will address several questions raised when addressing the task of building reliable classifier in the context of Personalized Medicine based on such High-Dimensional data (p ≫ N problems). Feature selection and Feature Stability are several of the issues we will discuss mainly experimentally. We will point out a strong empirical correlation between the dimensionality/sample size ratio and selection instability. Finally we will discuss an original algorithm to learn ternary weight classifiers well adapted to deal with metagenomics data.</small>
              <br><br>
              <small><b>Keywords:</b> High dimension Data Mining, Abstraction, Feature Selection, Feature Stability.
              </small>
              <br><hr><br>
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